OEP002321 dataset

Combined dataset

This file is used to generate a dataset containing all individual datasets, without melanocytes.

library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)

.libPaths()

## [1] "/usr/local/lib/R/library"

Preparation

In this section, we set the global settings of the analysis. We will store data there :

save_name = "wu"
out_dir = "."
n_threads = 5 # for tSNE

We load the sample information :

sample_info = readRDS(paste0(out_dir, "/../1_metadata/wu_sample_info.rds"))
project_names_oi = sample_info$project_name

graphics::pie(rep(1, nrow(sample_info)),
              col = sample_info$color,
              labels = sample_info$project_name)

Here are custom colors for each cell type :

color_markers = readRDS(paste0(out_dir, "/../1_metadata/wu_color_markers.rds"))

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

We load the markers and specific colors for each cell type :

cell_markers = readRDS(paste0(out_dir, "/../1_metadata/wu_cell_markers.rds"))
lengths(cell_markers)

##          CD4 T cells          CD8 T cells     Langerhans cells 
##                   13                   13                    9 
##          macrophages              B cells              cuticle 
##                   10                   16                   15 
##               cortex              medulla                  IRS 
##                   16                   10                   16 
##        proliferative               HF-SCs            IFE basal 
##                   20                   17                   16 
## IFE granular spinous                  ORS          melanocytes 
##                   17                   15                   10 
##            sebocytes 
##                    8

We load markers to display on the dotplot :

dotplot_markers = readRDS(paste0(out_dir, "/../1_metadata/wu_dotplot_markers.rds"))
dotplot_markers

## $`CD4 T cells`
## [1] "PTPRC" "CD3E"  "CD4"  
## 
## $`CD8 T cells`
## [1] "CD3E" "CD8A"
## 
## $`Langerhans cells`
## [1] "CD207" "CPVL" 
## 
## $macrophages
## [1] "TREM2" "MSR1" 
## 
## $`B cells`
## [1] "CD79A" "CD79B"
## 
## $cuticle
## [1] "MSX2"  "KRT32" "KRT35"
## 
## $cortex
## [1] "KRT31" "PRR9" 
## 
## $medulla
## [1] "BAMBI"   "ADLH1A3"
## 
## $IRS
## [1] "KRT71" "KRT73"
## 
## $proliferative
## [1] "TOP2A" "MCM5"  "TK1"  
## 
## $`HF-SCs`
## [1] "KRT14"  "CXCL14"
## 
## $`IFE basal`
## [1] "COL17A1" "KRT15"  
## 
## $`IFE granular spinous`
## [1] "SPINK5" "KRT1"  
## 
## $ORS
## [1] "KRT16" "KRT6C"
## 
## $melanocytes
## [1] "DCT"   "MLANA"
## 
## $sebocytes
## [1] "CLMP"  "PPARG"

Make wu dataset

Individual datasets

For each sample, we :

• load individual dataset
• look at cell annotation

We load individual datasets :

sobj_list = lapply(project_names_oi, FUN = function(one_project_name) {
  subsobj = readRDS(paste0(out_dir, "/../2_individual/datasets/",
                           one_project_name, "_sobj_filtered.rds"))
  return(subsobj)
})
names(sobj_list) = project_names_oi

lapply(sobj_list, FUN = dim) %>%
  do.call(rbind, .) %>%
  rbind(., colSums(.))

##        [,1]  [,2]
## F18   27955  1372
## F31B  27955  4786
## F31W  27955  3520
## F59   27955  2445
## F62B  27955  3279
## F62W  27955  2360
##      167730 17762

We represent cells in the tSNE :

name2D = "RNA_pca_20_tsne"

We look at cell type annotation for each dataset :

plot_list = lapply(sobj_list, FUN = function(one_sobj) {
  mytitle = as.character(unique(one_sobj$project_name))
  mysubtitle = ncol(one_sobj)
  
  p = Seurat::DimPlot(one_sobj, group.by = "cell_type",
                      reduction = name2D) +
    ggplot2::scale_color_manual(values = color_markers,
                                breaks = names(color_markers),
                                name = "Cell Type") +
    ggplot2::labs(title = mytitle,
                  subtitle = paste0(mysubtitle, " cells")) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5)) +
    Seurat::NoAxes()
  
  return(p)
})

plot_list[[length(plot_list) + 1]] = patchwork::guide_area()

patchwork::wrap_plots(plot_list, ncol = 4) +
  patchwork::plot_layout(guides = "collect") &
  ggplot2::theme(legend.position = "right")

and clustering :

plot_list = lapply(sobj_list, FUN = function(one_sobj) {
  mytitle = as.character(unique(one_sobj$project_name))
  mysubtitle = ncol(one_sobj)
  
  p = Seurat::DimPlot(one_sobj, group.by = "seurat_clusters",
                      reduction = name2D, label = TRUE) +
    ggplot2::labs(title = mytitle,
                  subtitle = paste0(mysubtitle, " cells")) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5)) +
    Seurat::NoAxes() + Seurat::NoLegend()
  
  return(p)
})

patchwork::wrap_plots(plot_list, ncol = 4)

Melanocytes remomal

For each individual dataset, we remove melanocytes. First, we smooth cell type annotation at a cluster level :

sobj_list = lapply(sobj_list, FUN = function(one_sobj) {
  cluster_type = table(one_sobj$cell_type, one_sobj$seurat_clusters) %>%
    prop.table(., margin = 2) %>%
    apply(., 2, which.max)
  cluster_type = setNames(nm = names(cluster_type),
                          levels(one_sobj$cell_type)[cluster_type])
  
  one_sobj$cluster_type = cluster_type[one_sobj$seurat_clusters]
  
  ## Output
  return(one_sobj)
})

To locate melanocytes, we look at their score, cell type annotation, and clustering.

plot_list = lapply(sobj_list, FUN = function(one_sobj) {
  project_name = as.character(unique(one_sobj$project_name))
  plot_sublist = list()
  
  # Score
  plot_sublist[[1]] = Seurat::FeaturePlot(one_sobj, reduction = name2D,
                                          features = "score_melanocytes") +
    ggplot2::labs(title = project_name,
                  subtitle = "Melanocytes score") +
    Seurat::NoAxes() +
    ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.subtitle = element_text(hjust = 0.5))
  
  # Cell type
  plot_sublist[[2]] = Seurat::DimPlot(one_sobj,
                                      reduction = name2D,
                                      group.by = "cell_type",
                                      order = "melanocytes") +
    ggplot2::scale_color_manual(values = c("purple", rep("gray92", length(color_markers) - 1)),
                                breaks = c("melanocytes", setdiff(names(color_markers), "melanocytes"))) +
    ggplot2::labs(title = "Cell type annotation",
                  subtitle = paste0(sum(one_sobj$cell_type == "melanocytes"),
                                    " melanocytes")) +
    Seurat::NoAxes() + Seurat::NoLegend() +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5))
  
  # Clusters
  plot_sublist[[3]] = Seurat::DimPlot(one_sobj,
                                      reduction = name2D,
                                      group.by = "seurat_clusters",
                                      label = TRUE) +
    ggplot2::labs(title = "Clusters") +
    Seurat::NoAxes() + Seurat::NoLegend() +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5))
  
  # Cluster type
  plot_sublist[[4]] = Seurat::DimPlot(one_sobj,
                                      reduction = name2D,
                                      group.by = "cluster_type") +
    ggplot2::scale_color_manual(values = c("purple", rep("gray92", length(color_markers) - 1)),
                                breaks = c("melanocytes", setdiff(names(color_markers), "melanocytes"))) +
    ggplot2::labs(title = "Cluster annotation",
                  subtitle = paste0(sum(one_sobj$cluster_type == "melanocytes"),
                                    " melanocytes")) +
    Seurat::NoAxes() + Seurat::NoLegend() +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5),
                   plot.subtitle = element_text(hjust = 0.5))
  
  return(plot_sublist)
}) %>% unlist(., recursive = FALSE)

patchwork::wrap_plots(plot_list, ncol = 4)

Melanocytes are only present in datasets with black hairs (F31B and F62B).

We remove melanocytes based on cluster annotation :

sobj_list = lapply(sobj_list, FUN = function(one_sobj) {
  one_sobj$is_of_interest = (one_sobj$cluster_type != "melanocytes")
  
  if (sum(one_sobj$is_of_interest) > 0) {
    one_sobj = subset(one_sobj, is_of_interest == TRUE)
  } else {
    one_sobj = NA
  }
  
  one_sobj$is_of_interest = NULL
  return(one_sobj)
})

lapply(sobj_list, FUN = dim) %>%
  do.call(rbind, .) %>%
  rbind(., colSums(.))

##        [,1]  [,2]
## F18   27955  1372
## F31B  27955  4624
## F31W  27955  3520
## F59   27955  2445
## F62B  27955  3221
## F62W  27955  2360
##      167730 17542

Re-annotation

We remove melanocytes from annotation :

cell_markers = cell_markers[names(cell_markers) != "melanocytes"]
color_markers = color_markers[names(color_markers) != "melanocytes"]
dotplot_markers = dotplot_markers[names(dotplot_markers) != "melanocytes"]

We re-annote cells for cell type, since melanocytes have been removed :

sobj_list = lapply(sobj_list, FUN = function(one_sobj) {
  # Remove old annotation
  one_sobj@meta.data[, grep(colnames(one_sobj@meta.data), pattern = "score", value = TRUE)] = NULL
  
  # Re-annot
  one_sobj = aquarius::cell_annot_custom(one_sobj,
                                         newname = "cell_type",
                                         markers = cell_markers,
                                         use_negative = TRUE,
                                         add_score = FALSE,
                                         verbose = TRUE)
  
  # Set factor levels
  one_sobj$cell_type = factor(one_sobj$cell_type, levels = names(cell_markers))
  
  return(one_sobj)
})

Combined dataset

We combine all datasets :

sobj = base::merge(sobj_list[[1]],
                   y = sobj_list[c(2:length(sobj_list))],
                   add.cell.ids = names(sobj_list))
sobj

## An object of class Seurat 
## 27955 features across 17542 samples within 1 assay 
## Active assay: RNA (27955 features, 0 variable features)

We add again the correspondence between gene names and gene ID. Since all datasets have been aligned using the same transcriptome, we take the correspondence from one individual dataset.

sobj@assays$RNA@meta.features = sobj_list[[1]]@assays$RNA@meta.features[, c("Ensembl_ID", "gene_name")]

head(sobj@assays$RNA@meta.features)

##                  Ensembl_ID   gene_name
## MIR1302-2HG ENSG00000243485 MIR1302-2HG
## FAM138A     ENSG00000237613     FAM138A
## OR4F5       ENSG00000186092       OR4F5
## AL627309.1  ENSG00000238009  AL627309.1
## AL627309.3  ENSG00000239945  AL627309.3
## AL627309.4  ENSG00000241599  AL627309.4

We remove the list of objects :

rm(sobj_list)

We keep a subset of meta.data and reset levels :

sobj@meta.data = sobj@meta.data[, c("orig.ident", "nCount_RNA", "nFeature_RNA", "log_nCount_RNA",
                                    "project_name", "sample_identifier", "sample_type",
                                    "laboratory", "location", "Seurat.Phase", "cyclone.Phase",
                                    "percent.mt", "percent.rb", "cell_type")]

sobj$orig.ident = factor(sobj$orig.ident, levels = levels(sample_info$project_name))
sobj$project_name = factor(sobj$project_name, levels = levels(sample_info$project_name))
sobj$sample_identifier = factor(sobj$sample_identifier, levels = levels(sample_info$sample_identifier))
sobj$sample_type = factor(sobj$sample_type, levels = levels(sample_info$sample_type))
sobj$cell_type = factor(sobj$cell_type, levels = names(color_markers))

summary(sobj@meta.data)

##  orig.ident    nCount_RNA      nFeature_RNA  log_nCount_RNA   project_name
##  F18 :1372   Min.   :   702   Min.   : 500   Min.   : 6.554   F18 :1372   
##  F31B:4624   1st Qu.:  2831   1st Qu.:1071   1st Qu.: 7.948   F31B:4624   
##  F31W:3520   Median :  7690   Median :2027   Median : 8.948   F31W:3520   
##  F59 :2445   Mean   :  9867   Mean   :2097   Mean   : 8.748   F59 :2445   
##  F62B:3221   3rd Qu.: 13990   3rd Qu.:2897   3rd Qu.: 9.546   F62B:3221   
##  F62W:2360   Max.   :125066   Max.   :7538   Max.   :11.737   F62W:2360   
##                                                                           
##  sample_identifier sample_type   laboratory          location        
##  Wu_HD_1:1372      NA's:17542   Length:17542       Length:17542      
##  Wu_HD_2:4624                   Class :character   Class :character  
##  Wu_HD_3:3520                   Mode  :character   Mode  :character  
##  Wu_HD_4:2445                                                        
##  Wu_HD_5:3221                                                        
##  Wu_HD_6:2360                                                        
##                                                                      
##  Seurat.Phase       cyclone.Phase        percent.mt       percent.rb     
##  Length:17542       Length:17542       Min.   : 0.000   Min.   : 0.8197  
##  Class :character   Class :character   1st Qu.: 3.227   1st Qu.:18.0827  
##  Mode  :character   Mode  :character   Median : 5.119   Median :23.0829  
##                                        Mean   : 6.134   Mean   :22.3492  
##                                        3rd Qu.: 8.023   3rd Qu.:27.2938  
##                                        Max.   :20.000   Max.   :47.5605  
##                                                                          
##                 cell_type   
##  ORS                 :6773  
##  IFE granular spinous:3272  
##  cortex              :1809  
##  cuticle             :1532  
##  IFE basal           :1210  
##  proliferative       : 809  
##  (Other)             :2137

Processing

We remove genes that are expressed in less than 5 cells :

sobj = aquarius::filter_features(sobj, min_cells = 5)

## [1] 27955 17542
## [1] 17604 17542

sobj

## An object of class Seurat 
## 17604 features across 17542 samples within 1 assay 
## Active assay: RNA (17604 features, 0 variable features)

Metadata

How many cells by sample ?

table(sobj$project_name)

## 
##  F18 F31B F31W  F59 F62B F62W 
## 1372 4624 3520 2445 3221 2360

We represent this information as a barplot :

aquarius::plot_barplot(df = table(sobj$project_name,
                                  sobj$cell_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_fill()) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type")

This is the same barplot with another position :

aquarius::plot_barplot(df = table(sobj$project_name,
                                  sobj$cell_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_stack()) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type")

Projection

We normalize the count matrix for remaining cells and select highly variable features :

sobj = Seurat::NormalizeData(sobj,
                             normalization.method = "LogNormalize")
sobj = Seurat::FindVariableFeatures(sobj, nfeatures = 2000)
sobj = Seurat::ScaleData(sobj)

sobj

## An object of class Seurat 
## 17604 features across 17542 samples within 1 assay 
## Active assay: RNA (17604 features, 2000 variable features)

We perform a PCA :

sobj = Seurat::RunPCA(sobj,
                      assay = "RNA",
                      reduction.name = "RNA_pca",
                      npcs = 100,
                      seed.use = 1337L)
sobj

## An object of class Seurat 
## 17604 features across 17542 samples within 1 assay 
## Active assay: RNA (17604 features, 2000 variable features)
##  1 dimensional reduction calculated: RNA_pca

We choose the number of dimensions such that they summarize 60 % of the variability :

stdev = sobj@reductions[["RNA_pca"]]@stdev
stdev_prop = cumsum(stdev)/sum(stdev)
ndims = which(stdev_prop > 0.60)[1]
ndims

## [1] 39

We can visualize this on the elbow plot :

elbow_p = Seurat::ElbowPlot(sobj, ndims = 100, reduction = "RNA_pca") +
  ggplot2::geom_point(x = ndims, y = stdev[ndims], col = "red")
x_text = ggplot_build(elbow_p)$layout$panel_params[[1]]$x$get_labels() %>% as.numeric()
elbow_p = elbow_p +
  ggplot2::scale_x_continuous(breaks = sort(c(x_text, ndims)), limits = c(0, 100))
x_color = ifelse(ggplot_build(elbow_p)$layout$panel_params[[1]]$x$get_labels() %>%
                   as.numeric() %>% round(., 2) == round(ndims, 2), "red", "black")
elbow_p = elbow_p +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.x = element_text(color = x_color))

elbow_p

We generate a tSNE and a UMAP with 39 principal components :

sobj = Seurat::RunTSNE(sobj,
                       reduction = "RNA_pca",
                       dims = 1:ndims,
                       seed.use = 1337L,
                       num_threads = n_threads, # Rtsne::Rtsne option
                       reduction.name = paste0("RNA_pca_", ndims, "_tsne"))

sobj = Seurat::RunUMAP(sobj,
                       reduction = "RNA_pca",
                       dims = 1:ndims,
                       seed.use = 1337L,
                       reduction.name = paste0("RNA_pca_", ndims, "_umap"))

(Time to run : 64.18 s)

We can visualize the two representations :

tsne = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("RNA_pca_", ndims, "_tsne")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - tSNE") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 legend.position = "none")

umap = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("RNA_pca_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

tsne | umap

Batch-effect correction

We remove sample specific effect on the pca using Harmony :

`%||%` = function(lhs, rhs) {
  if (!is.null(x = lhs)) {
    return(lhs)
  } else {
    return(rhs)
  }
}

set.seed(1337L)
sobj = harmony::RunHarmony(object = sobj,
                           group.by.vars = "project_name",
                           plot_convergence = TRUE,
                           reduction = "RNA_pca",
                           assay.use = "RNA",
                           reduction.save = "harmony",
                           max.iter.harmony = 50,
                           project.dim = FALSE)

(Time to run : 111.44 s)

From this batch-effect removed projection, we generate a tSNE and a UMAP.

sobj = Seurat::RunUMAP(sobj, 
                       seed.use = 1337L,
                       dims = 1:ndims,
                       reduction = "harmony",
                       reduction.name = paste0("harmony_", ndims, "_umap"),
                       reduction.key = paste0("harmony_", ndims, "umap_"))

sobj = Seurat::RunTSNE(sobj,
                       dims = 1:ndims,
                       seed.use = 1337L,
                       num_threads = n_threads, # Rtsne::Rtsne option
                       reduction = "harmony",
                       reduction.name = paste0("harmony_", ndims, "_tsne"),
                       reduction.key = paste0("harmony", ndims, "tsne_"))

(Time to run : 67.09 s)

We visualize the corrected projections :

tsne = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("harmony_", ndims, "_tsne")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - harmony - tSNE") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 legend.position = "none")

umap = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("harmony_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - harmony - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

tsne | umap

We will keep the tSNE from harmony :

reduction = "harmony"
name2D = paste0("harmony_", ndims, "_tsne")

Clustering

We generate a clustering :

sobj = Seurat::FindNeighbors(sobj, reduction = reduction, dims = 1:ndims)
sobj = Seurat::FindClusters(sobj, resolution = 1.5)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17542
## Number of edges: 728090
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8497
## Number of communities: 33
## Elapsed time: 2 seconds

clusters_plot = Seurat::DimPlot(sobj, reduction = name2D, label = TRUE) +
  Seurat::NoAxes() + Seurat::NoLegend() +
  ggplot2::labs(title = "Clusters ID") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))
clusters_plot

Visualization

We represent the 4 quality metrics :

plot_list = Seurat::FeaturePlot(sobj, reduction = name2D,
                                combine = FALSE, pt.size = 0.25,
                                features = c("percent.mt", "percent.rb", "log_nCount_RNA", "nFeature_RNA"))
plot_list = lapply(plot_list, FUN = function(one_plot) {
  one_plot +
    Seurat::NoAxes() +
    ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
    ggplot2::theme(aspect.ratio = 1)
})

patchwork::wrap_plots(plot_list, nrow = 1)

Clusters

We can represent clusters, split by sample of origin :

plot_list = aquarius::plot_split_dimred(sobj,
                                        reduction = name2D,
                                        split_by = "project_name",
                                        group_by = "seurat_clusters",
                                        split_color = setNames(sample_info$color,
                                                               nm = sample_info$project_name),
                                        group_color = aquarius::gg_color_hue(length(levels(sobj$seurat_clusters))))

plot_list[[length(plot_list) + 1]] = clusters_plot +
  ggplot2::labs(title = "Cluster ID") &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, size = 15))

patchwork::wrap_plots(plot_list, ncol = 4) &
  Seurat::NoLegend()

Cell type

We visualize cell type :

plot_list = lapply((c(paste0("RNA_pca_", ndims, "_tsne"),
                      paste0("RNA_pca_", ndims, "_umap"),
                      paste0("harmony_", ndims, "_tsne"),
                      paste0("harmony_", ndims, "_umap"))),
                   FUN = function(one_red) {
                     Seurat::DimPlot(sobj, group.by = "cell_type",
                                     reduction = one_red,
                                     cols = color_markers) +
                       Seurat::NoAxes() + ggplot2::ggtitle(one_red) +
                       ggplot2::theme(aspect.ratio = 1,
                                      plot.title = element_text(hjust = 0.5))
                   })

patchwork::wrap_plots(plot_list, nrow = 2) +
  patchwork::plot_layout(guides = "collect")

We make a representation split by origin to show cell types :

plot_list = aquarius::plot_split_dimred(sobj,
                                        reduction = name2D,
                                        split_by = "project_name",
                                        split_color = setNames(sample_info$color,
                                                               nm = sample_info$project_name),
                                        group_by = "cell_type",
                                        group_color = color_markers)

plot_list[[length(plot_list) + 1]] = patchwork::guide_area()

patchwork::wrap_plots(plot_list, ncol = 4) +
  patchwork::plot_layout(guides = "collect") &
  ggplot2::theme(legend.position = "right")

Cell cycle

We visualize cell cycle annotation, and BIRC5 and TOP2A expression levels :

plot_list = list()

# Seurat
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
                                 reduction = name2D) +
  Seurat::NoAxes() + ggplot2::labs(title = "Seurat annotation") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

# cyclone
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
                                 reduction = name2D) +
  Seurat::NoAxes() + ggplot2::labs(title = "cyclone annotation") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

# BIRC5
plot_list[[3]] = Seurat::FeaturePlot(sobj, features = "BIRC5",
                                     reduction = name2D) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

# TOP2A
plot_list[[4]] = Seurat::FeaturePlot(sobj, features = "TOP2A",
                                     reduction = name2D) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

patchwork::wrap_plots(plot_list, ncol = 2)

Save

We save the Seurat object :

saveRDS(sobj, file = paste0(out_dir, "/", save_name, "_sobj.rds"))

R Session

show

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
## 
## locale:
## [1] C
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] ComplexHeatmap_2.14.0 ggplot2_3.3.5         patchwork_1.1.2      
## [4] dplyr_1.0.7          
## 
## loaded via a namespace (and not attached):
##   [1] softImpute_1.4              graphlayouts_0.7.0         
##   [3] pbapply_1.4-2               lattice_0.20-41            
##   [5] haven_2.3.1                 vctrs_0.3.8                
##   [7] usethis_2.0.1               dynwrap_1.2.1              
##   [9] blob_1.2.1                  survival_3.2-13            
##  [11] prodlim_2019.11.13          dynutils_1.0.5             
##  [13] later_1.3.0                 DBI_1.1.1                  
##  [15] R.utils_2.11.0              SingleCellExperiment_1.8.0 
##  [17] rappdirs_0.3.3              uwot_0.1.8                 
##  [19] dqrng_0.2.1                 jpeg_0.1-8.1               
##  [21] zlibbioc_1.32.0             pspline_1.0-18             
##  [23] pcaMethods_1.78.0           mvtnorm_1.1-1              
##  [25] htmlwidgets_1.5.4           GlobalOptions_0.1.2        
##  [27] future_1.22.1               UpSetR_1.4.0               
##  [29] laeken_0.5.2                leiden_0.3.3               
##  [31] clustree_0.4.3              parallel_3.6.3             
##  [33] scater_1.14.6               irlba_2.3.3                
##  [35] DEoptimR_1.0-9              tidygraph_1.1.2            
##  [37] Rcpp_1.0.9                  readr_2.0.2                
##  [39] KernSmooth_2.23-17          carrier_0.1.0              
##  [41] promises_1.1.0              gdata_2.18.0               
##  [43] DelayedArray_0.12.3         limma_3.42.2               
##  [45] graph_1.64.0                RcppParallel_5.1.4         
##  [47] Hmisc_4.4-0                 fs_1.5.2                   
##  [49] RSpectra_0.16-0             fastmatch_1.1-0            
##  [51] ranger_0.12.1               digest_0.6.25              
##  [53] png_0.1-7                   sctransform_0.2.1          
##  [55] cowplot_1.0.0               DOSE_3.12.0                
##  [57] here_1.0.1                  TInGa_0.0.0.9000           
##  [59] ggraph_2.0.3                pkgconfig_2.0.3            
##  [61] GO.db_3.10.0                DelayedMatrixStats_1.8.0   
##  [63] gower_0.2.1                 ggbeeswarm_0.6.0           
##  [65] iterators_1.0.12            DropletUtils_1.6.1         
##  [67] reticulate_1.26             clusterProfiler_3.14.3     
##  [69] SummarizedExperiment_1.16.1 circlize_0.4.15            
##  [71] beeswarm_0.4.0              GetoptLong_1.0.5           
##  [73] xfun_0.35                   bslib_0.3.1                
##  [75] zoo_1.8-10                  tidyselect_1.1.0           
##  [77] reshape2_1.4.4              purrr_0.3.4                
##  [79] ica_1.0-2                   pcaPP_1.9-73               
##  [81] viridisLite_0.3.0           rtracklayer_1.46.0         
##  [83] rlang_1.0.2                 hexbin_1.28.1              
##  [85] jquerylib_0.1.4             dyneval_0.9.9              
##  [87] glue_1.4.2                  RColorBrewer_1.1-2         
##  [89] matrixStats_0.56.0          stringr_1.4.0              
##  [91] lava_1.6.7                  europepmc_0.3              
##  [93] DESeq2_1.26.0               recipes_0.1.17             
##  [95] labeling_0.3                harmony_0.1.0              
##  [97] httpuv_1.5.2                class_7.3-17               
##  [99] BiocNeighbors_1.4.2         DO.db_2.9                  
## [101] annotate_1.64.0             jsonlite_1.7.2             
## [103] XVector_0.26.0              bit_4.0.4                  
## [105] mime_0.9                    aquarius_0.1.5             
## [107] Rsamtools_2.2.3             gridExtra_2.3              
## [109] gplots_3.0.3                stringi_1.4.6              
## [111] processx_3.5.2              gsl_2.1-6                  
## [113] bitops_1.0-6                cli_3.0.1                  
## [115] batchelor_1.2.4             RSQLite_2.2.0              
## [117] randomForest_4.6-14         tidyr_1.1.4                
## [119] data.table_1.14.2           rstudioapi_0.13            
## [121] org.Mm.eg.db_3.10.0         GenomicAlignments_1.22.1   
## [123] nlme_3.1-147                qvalue_2.18.0              
## [125] scran_1.14.6                locfit_1.5-9.4             
## [127] scDblFinder_1.1.8           listenv_0.8.0              
## [129] ggthemes_4.2.4              gridGraphics_0.5-0         
## [131] R.oo_1.24.0                 dbplyr_1.4.4               
## [133] BiocGenerics_0.32.0         TTR_0.24.2                 
## [135] readxl_1.3.1                lifecycle_1.0.1            
## [137] timeDate_3043.102           ggpattern_0.3.1            
## [139] munsell_0.5.0               cellranger_1.1.0           
## [141] R.methodsS3_1.8.1           proxyC_0.1.5               
## [143] visNetwork_2.0.9            caTools_1.18.0             
## [145] codetools_0.2-16            Biobase_2.46.0             
## [147] GenomeInfoDb_1.22.1         vipor_0.4.5                
## [149] lmtest_0.9-38               msigdbr_7.5.1              
## [151] htmlTable_1.13.3            triebeard_0.3.0            
## [153] lsei_1.2-0                  xtable_1.8-4               
## [155] ROCR_1.0-7                  BiocManager_1.30.10        
## [157] scatterplot3d_0.3-41        abind_1.4-5                
## [159] farver_2.0.3                parallelly_1.28.1          
## [161] RANN_2.6.1                  askpass_1.1                
## [163] GenomicRanges_1.38.0        RcppAnnoy_0.0.16           
## [165] tibble_3.1.5                ggdendro_0.1-20            
## [167] cluster_2.1.0               future.apply_1.5.0         
## [169] Seurat_3.1.5                dendextend_1.15.1          
## [171] Matrix_1.3-2                ellipsis_0.3.2             
## [173] prettyunits_1.1.1           lubridate_1.7.9            
## [175] ggridges_0.5.2              igraph_1.2.5               
## [177] RcppEigen_0.3.3.7.0         fgsea_1.12.0               
## [179] remotes_2.4.2               scBFA_1.0.0                
## [181] destiny_3.0.1               VIM_6.1.1                  
## [183] testthat_3.1.0              htmltools_0.5.2            
## [185] BiocFileCache_1.10.2        yaml_2.2.1                 
## [187] utf8_1.1.4                  plotly_4.9.2.1             
## [189] XML_3.99-0.3                ModelMetrics_1.2.2.2       
## [191] e1071_1.7-3                 foreign_0.8-76             
## [193] withr_2.5.0                 fitdistrplus_1.0-14        
## [195] BiocParallel_1.20.1         xgboost_1.4.1.1            
## [197] bit64_4.0.5                 foreach_1.5.0              
## [199] robustbase_0.93-9           Biostrings_2.54.0          
## [201] GOSemSim_2.13.1             rsvd_1.0.3                 
## [203] memoise_2.0.0               evaluate_0.18              
## [205] forcats_0.5.0               rio_0.5.16                 
## [207] geneplotter_1.64.0          tzdb_0.1.2                 
## [209] caret_6.0-86                ps_1.6.0                   
## [211] DiagrammeR_1.0.6.1          curl_4.3                   
## [213] fdrtool_1.2.15              fansi_0.4.1                
## [215] highr_0.8                   urltools_1.7.3             
## [217] xts_0.12.1                  GSEABase_1.48.0            
## [219] acepack_1.4.1               edgeR_3.28.1               
## [221] checkmate_2.0.0             scds_1.2.0                 
## [223] cachem_1.0.6                npsurv_0.4-0               
## [225] babelgene_22.3              rjson_0.2.20               
## [227] openxlsx_4.1.5              ggrepel_0.9.1              
## [229] clue_0.3-60                 rprojroot_2.0.2            
## [231] stabledist_0.7-1            tools_3.6.3                
## [233] sass_0.4.0                  nichenetr_1.1.1            
## [235] magrittr_2.0.1              RCurl_1.98-1.2             
## [237] proxy_0.4-24                car_3.0-11                 
## [239] ape_5.3                     ggplotify_0.0.5            
## [241] xml2_1.3.2                  httr_1.4.2                 
## [243] assertthat_0.2.1            rmarkdown_2.18             
## [245] boot_1.3-25                 globals_0.14.0             
## [247] R6_2.4.1                    Rhdf5lib_1.8.0             
## [249] nnet_7.3-14                 RcppHNSW_0.2.0             
## [251] progress_1.2.2              genefilter_1.68.0          
## [253] statmod_1.4.34              gtools_3.8.2               
## [255] shape_1.4.6                 HDF5Array_1.14.4           
## [257] BiocSingular_1.2.2          rhdf5_2.30.1               
## [259] splines_3.6.3               AUCell_1.8.0               
## [261] carData_3.0-4               colorspace_1.4-1           
## [263] generics_0.1.0              stats4_3.6.3               
## [265] base64enc_0.1-3             dynfeature_1.0.0           
## [267] smoother_1.1                gridtext_0.1.1             
## [269] pillar_1.6.3                tweenr_1.0.1               
## [271] sp_1.4-1                    ggplot.multistats_1.0.0    
## [273] rvcheck_0.1.8               GenomeInfoDbData_1.2.2     
## [275] plyr_1.8.6                  gtable_0.3.0               
## [277] zip_2.2.0                   knitr_1.41                 
## [279] latticeExtra_0.6-29         biomaRt_2.42.1             
## [281] IRanges_2.20.2              fastmap_1.1.0              
## [283] ADGofTest_0.3               copula_1.0-0               
## [285] doParallel_1.0.15           AnnotationDbi_1.48.0       
## [287] vcd_1.4-8                   babelwhale_1.0.1           
## [289] openssl_1.4.1               scales_1.1.1               
## [291] backports_1.2.1             S4Vectors_0.24.4           
## [293] ipred_0.9-12                enrichplot_1.6.1           
## [295] hms_1.1.1                   ggforce_0.3.1              
## [297] Rtsne_0.15                  shiny_1.7.1                
## [299] numDeriv_2016.8-1.1         polyclip_1.10-0            
## [301] lazyeval_0.2.2              Formula_1.2-3              
## [303] tsne_0.1-3                  crayon_1.3.4               
## [305] MASS_7.3-54                 pROC_1.16.2                
## [307] viridis_0.5.1               dynparam_1.0.0             
## [309] rpart_4.1-15                zinbwave_1.8.0             
## [311] compiler_3.6.3              ggtext_0.1.0